Benjamin, Lucas; Sablé-Meyer, Mathias; Fló, Ana; Dehaene-Lambertz, Ghislaine; Roumi, Fosca Al; (2024) Long-horizon associative learning explains human sensitivity to statistical and network structures in auditory sequences. Journal of Neuroscience , Article e1369232024. 10.1523/JNEUROSCI.1369-23.2024. (In press).

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Abstract

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults (N=23, 16 females) were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and non-adjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.Significance statement We conducted a MEG study in which human adults were passively exposed to sequences of tones organized in a sparse community network structure. Despite the uniform transition probabilities between tones, participants' brain activity exhibited sensitivity to the network structure. Notably, a consistent "deviant" response was observed at ∼150 ms when the sequence switched between communities. A long-tail exponential decay in tone representation allowed overlapping representations of successive sequence elements, facilitating long-range associative mechanisms. This binding mechanism adequately accounted for various scales of sequence learning, bridging the gap between statistical and network learning approaches.

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